The causal view of categories assumes that categories are represented by features and their causal relations. To study the effect of causal knowledge on categorization, researchers have used Bayesian causal models. Within that framework, categorization may be viewed as dependent on a likelihood computation (i.e., the likelihood of an exemplar with a certain combination of features, given the category's causal model) or as a posterior computation (i.e., the probability that the exemplar belongs to the category, given its features). Across three (...) experiments, in combination with computational modeling, we offer evidence that categorization is better accounted for by assuming that people compute posteriors and not likelihoods, though both probabilities are closely related. This result contrasts with existing analyses of causal-based categorization, which assume that likelihood computations give a good approximation of human judgments. We also find that people are able to compute likelihoods in a closely related task that elicits judgments of consistency rather than category membership judgments. Our analyses show that people do use causal probabilistic information as prescribed by a Bayesian model but that they flexibly compute likelihoods or posteriors depending on the task. We discuss our results in relation to the relevant literature on the topic. (shrink)

Human causal judgments frequently deviate from normative Bayesian expectations, particularly with respect to conditional independence and explaining away. Rather than interpreting these deviations as reasoning errors, recent computational accounts suggest they may emerge from principled approximations to ideal Bayesian inference. We evaluate four leading frameworks: Bayesian sampler (BS), mutation sampler (MS), Bayesian mutation sampler (BMS), and Bayesian uncertainty model (BUM), which each formalize different cognitive constraints, including limited sampling, prototype anchoring, prior regularization, and uncertainty over causal structure. These models were (...) systematically compared across 33 reported experimental conditions (N = 1154) spanning diverse causal structures, including common cause, common effect, and chain networks with generative and inhibitory relations. All four models had at least some success reproducing the positive and negative Markov violations observed in common cause and chain structures. In common effect structures with inhibitory or mixed links, only MS and BMS captured the observed patterns more consistently, although BMS's additional parameter offered limited improvement over MS. BS and BUM showed poorer fits in those conditions, though they may still offer plausible accounts under different assumptions. We discuss these findings in light of prior work on causal reasoning, emphasizing that deviations from normative inference may reflect adaptive strategies shaped by structural uncertainty and cognitive constraints. This supports a pluralistic and resource‐rational view of causal reasoning and underscores the need for targeted experiments probing inhibitory causal relations and model uncertainty. (shrink)